abstract revised for SupportVectorMachines.ipynb

doc/ipython-notebooks/classification/SupportVectorMachines.ipynb

@@ -1,6 +1,7 @@
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@@ -27,8 +28,7 @@
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-      "This notebook illustrates how to train a binary support vector machine (SVM) classifier with shogun. \n",
-      "A classifier attempts to distinguish objects of different type. In case of of binary classification there are just two types of objects that we want to distinguish."
+      "This notebook illustrates how to train a binary <a href=\"http://en.wikipedia.org/wiki/Support_vector_machine\">support vector machines</a> (SVM) <a href=\"http://en.wikipedia.org/wiki/Statistical_classification\">classifier</a> using shogun. Here the training and testing data is generated by sampling from a <a href=\"http://en.wikipedia.org/wiki/Mixture_model#Gaussian_mixture_model\">gaussian mixture model</a> (GMM). <a href=\"http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CLibSVM.html\">CLibSVM</a> class of shogun is used to train with this <a href=\"http://en.wikipedia.org/wiki/Sampled_Gaussian_kernel#The_sampled_Gaussian_kernel\">sampled Gaussian Kernel</a>. "
      ]
     },
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